/*-
 *  * Copyright 2016 Skymind, Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://www.apache.org/licenses/LICENSE-2.0
 *  *
 *  *    Unless required by applicable law or agreed to in writing, software
 *  *    distributed under the License is distributed on an "AS IS" BASIS,
 *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  *    See the License for the specific language governing permissions and
 *  *    limitations under the License.
 */

package org.datavec.audio.extension;


import org.datavec.audio.dsp.FastFourierTransform;
import org.datavec.audio.dsp.WindowFunction;
import org.datavec.audio.Wave;

/**
 * Handles the wave data in frequency-time domain.
 *
 * @author Jacquet Wong
 */
public class Spectrogram {

    public static final int SPECTROGRAM_DEFAULT_FFT_SAMPLE_SIZE = 1024;
    public static final int SPECTROGRAM_DEFAULT_OVERLAP_FACTOR = 0; // 0 for no overlapping

    private Wave wave;
    private double[][] spectrogram; // relative spectrogram
    private double[][] absoluteSpectrogram; // absolute spectrogram
    private int fftSampleSize; // number of sample in fft, the value needed to be a number to power of 2
    private int overlapFactor; // 1/overlapFactor overlapping, e.g. 1/4=25% overlapping
    private int numFrames; // number of frames of the spectrogram
    private int framesPerSecond; // frame per second of the spectrogram
    private int numFrequencyUnit; // number of y-axis unit
    private double unitFrequency; // frequency per y-axis unit

    /**
     * Constructor
     *
     * @param wave
     */
    public Spectrogram(Wave wave) {
        this.wave = wave;
        // default
        this.fftSampleSize = SPECTROGRAM_DEFAULT_FFT_SAMPLE_SIZE;
        this.overlapFactor = SPECTROGRAM_DEFAULT_OVERLAP_FACTOR;
        buildSpectrogram();
    }

    /**
     * Constructor
     *
     * @param wave
     * @param fftSampleSize	number of sample in fft, the value needed to be a number to power of 2
     * @param overlapFactor	1/overlapFactor overlapping, e.g. 1/4=25% overlapping, 0 for no overlapping
     */
    public Spectrogram(Wave wave, int fftSampleSize, int overlapFactor) {
        this.wave = wave;

        if (Integer.bitCount(fftSampleSize) == 1) {
            this.fftSampleSize = fftSampleSize;
        } else {
            System.err.print("The input number must be a power of 2");
            this.fftSampleSize = SPECTROGRAM_DEFAULT_FFT_SAMPLE_SIZE;
        }

        this.overlapFactor = overlapFactor;

        buildSpectrogram();
    }

    /**
     * Build spectrogram
     */
    private void buildSpectrogram() {

        short[] amplitudes = wave.getSampleAmplitudes();
        int numSamples = amplitudes.length;

        int pointer = 0;
        // overlapping
        if (overlapFactor > 1) {
            int numOverlappedSamples = numSamples * overlapFactor;
            int backSamples = fftSampleSize * (overlapFactor - 1) / overlapFactor;
            int fftSampleSize_1 = fftSampleSize - 1;
            short[] overlapAmp = new short[numOverlappedSamples];
            pointer = 0;
            for (int i = 0; i < amplitudes.length; i++) {
                overlapAmp[pointer++] = amplitudes[i];
                if (pointer % fftSampleSize == fftSampleSize_1) {
                    // overlap
                    i -= backSamples;
                }
            }
            numSamples = numOverlappedSamples;
            amplitudes = overlapAmp;
        }
        // end overlapping

        numFrames = numSamples / fftSampleSize;
        framesPerSecond = (int) (numFrames / wave.length());

        // set signals for fft
        WindowFunction window = new WindowFunction();
        window.setWindowType("Hamming");
        double[] win = window.generate(fftSampleSize);

        double[][] signals = new double[numFrames][];
        for (int f = 0; f < numFrames; f++) {
            signals[f] = new double[fftSampleSize];
            int startSample = f * fftSampleSize;
            for (int n = 0; n < fftSampleSize; n++) {
                signals[f][n] = amplitudes[startSample + n] * win[n];
            }
        }
        // end set signals for fft

        absoluteSpectrogram = new double[numFrames][];
        // for each frame in signals, do fft on it
        FastFourierTransform fft = new FastFourierTransform();
        for (int i = 0; i < numFrames; i++) {
            absoluteSpectrogram[i] = fft.getMagnitudes(signals[i]);
        }

        if (absoluteSpectrogram.length > 0) {

            numFrequencyUnit = absoluteSpectrogram[0].length;
            unitFrequency = (double) wave.getWaveHeader().getSampleRate() / 2 / numFrequencyUnit; // frequency could be caught within the half of nSamples according to Nyquist theory

            // normalization of absoultSpectrogram
            spectrogram = new double[numFrames][numFrequencyUnit];

            // set max and min amplitudes
            double maxAmp = Double.MIN_VALUE;
            double minAmp = Double.MAX_VALUE;
            for (int i = 0; i < numFrames; i++) {
                for (int j = 0; j < numFrequencyUnit; j++) {
                    if (absoluteSpectrogram[i][j] > maxAmp) {
                        maxAmp = absoluteSpectrogram[i][j];
                    } else if (absoluteSpectrogram[i][j] < minAmp) {
                        minAmp = absoluteSpectrogram[i][j];
                    }
                }
            }
            // end set max and min amplitudes

            // normalization
            // avoiding divided by zero 
            double minValidAmp = 0.00000000001F;
            if (minAmp == 0) {
                minAmp = minValidAmp;
            }

            double diff = Math.log10(maxAmp / minAmp); // perceptual difference
            for (int i = 0; i < numFrames; i++) {
                for (int j = 0; j < numFrequencyUnit; j++) {
                    if (absoluteSpectrogram[i][j] < minValidAmp) {
                        spectrogram[i][j] = 0;
                    } else {
                        spectrogram[i][j] = (Math.log10(absoluteSpectrogram[i][j] / minAmp)) / diff;
                    }
                }
            }
            // end normalization
        }
    }

    /**
     * Get spectrogram: spectrogram[time][frequency]=intensity
     *
     * @return	logarithm normalized spectrogram
     */
    public double[][] getNormalizedSpectrogramData() {
        return spectrogram;
    }

    /**
     * Get spectrogram: spectrogram[time][frequency]=intensity
     *
     * @return	absolute spectrogram
     */
    public double[][] getAbsoluteSpectrogramData() {
        return absoluteSpectrogram;
    }

    public int getNumFrames() {
        return numFrames;
    }

    public int getFramesPerSecond() {
        return framesPerSecond;
    }

    public int getNumFrequencyUnit() {
        return numFrequencyUnit;
    }

    public double getUnitFrequency() {
        return unitFrequency;
    }

    public int getFftSampleSize() {
        return fftSampleSize;
    }

    public int getOverlapFactor() {
        return overlapFactor;
    }
}
